A neuron-inspired computational architecture for spatiotemporal visual processing: real-time visual sensory integration for humanoid robots.

Abstract:

:In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to ∼+6%).

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Holzbach A,Cheng G

doi

10.1007/s00422-014-0597-3

subject

Has Abstract

pub_date

2014-06-01 00:00:00

pages

249-59

issue

3

eissn

0340-1200

issn

1432-0770

journal_volume

108

pub_type

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